An STDP-Based Supervised Learning Algorithm for Spiking Neural Networks

  • Zhanhao Hu
  • Tao Wang
  • Xiaolin HuEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10635)


Compared with rate-based artificial neural networks, Spiking Neural Networks (SNN) provide a more biological plausible model for the brain. But how they perform supervised learning remains elusive. Inspired by recent works of Bengio et al., we propose a supervised learning algorithm based on Spike-Timing Dependent Plasticity (STDP) for a hierarchical SNN consisting of Leaky Integrate-and-fire (LIF) neurons. A time window is designed for the presynaptic neuron and only the spikes in this window take part in the STDP updating process. The model is trained on the MNIST dataset. The classification accuracy approach that of a Multilayer Perceptron (MLP) with similar architecture trained by the standard back-propagation algorithm.


STDP SNN Supervised learning 



This work was supported in part by the National Natural Science Foundation of China under Grant 91420201, Grant 61332007, Grant 61621136008 and Grant 61620106010, in part by the Beijing Municipal Science and Technology Commission under Grant Z161100000216126, and in part by Huawei Technology under Contract YB2015120018.


  1. 1.
    Bengio, Y., Mesnard, T., Fischer, A., Zhang, S., Wu, Y.: STDP as presynaptic activity times rate of change of postsynaptic activity. arXiv preprint (2015). arXiv:1509.05936
  2. 2.
    Bohte, S.M., Kok, J.N., La Poutre, H.: Error-backpropagation in temporally encoded networks of spiking neurons. Neurocomputing 48(1), 17–37 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Booij, O., tat Nguyen, H.: A gradient descent rule for spiking neurons emitting multiple spikes. Inf. Process. Lett. 95(6), 552–558 (2005)CrossRefzbMATHMathSciNetGoogle Scholar
  4. 4.
    Clopath, C., Büsing, L., Vasilaki, E., Gerstner, W.: Connectivity reflects coding: a model of voltage-based STDP with Homeostasis. Nat. Neurosci. 13(3), 344–352 (2010)CrossRefGoogle Scholar
  5. 5.
    Dayan, P., Abbott, L.F.: Theoretical Neuroscience, vol. 806. MIT Press, Cambridge (2001)zbMATHGoogle Scholar
  6. 6.
    Diehl, P.U., Cook, M.: Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front. Comput. Neurosci. 9, 99 (2015)CrossRefGoogle Scholar
  7. 7.
    Ghosh-Dastidar, S., Adeli, H.: A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection. Neural Netw. 22(10), 1419–1431 (2009)CrossRefGoogle Scholar
  8. 8.
    Gütig, R., Sompolinsky, H.: The tempotron: a neuron that learns spike timing-based decisions. Nat. Neurosci. 9(3), 420–428 (2006)CrossRefGoogle Scholar
  9. 9.
    Kheradpisheh, S.R., Ganjtabesh, M., Thorpe, S.J., Masquelier, T.: STDP-based spiking deep neural networks for object recognition. arXiv preprint (2016). arXiv:1611.01421
  10. 10.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  11. 11.
    Markram, H., Lübke, J., Frotscher, M., Sakmann, B.: Regulation of synaptic efficacy by coincidence of postsynaptic APS and EPSPS. Science 275(5297), 213–215 (1997)CrossRefGoogle Scholar
  12. 12.
    Ponulak, F., Kasiński, A.: Supervised learning in spiking neural networks with resume: sequence learning, classification, and spike shifting. Neural Comput. 22(2), 467–510 (2010)CrossRefzbMATHMathSciNetGoogle Scholar
  13. 13.
    Querlioz, D., Bichler, O., Dollfus, P., Gamrat, C.: Immunity to device variations in a spiking neural network with memristive nanodevices. IEEE Trans. Nanotechnol. 12(3), 288–295 (2013)CrossRefGoogle Scholar
  14. 14.
    Scellier, B., Bengio, Y.: Equilibrium propagation: bridging the gap between energy-based models and backpropagation. Front. Comput. Neurosci. 11 (2017). Article no. 24Google Scholar
  15. 15.
    Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat. Neurosci. 3(9), 919–926 (2000)CrossRefGoogle Scholar
  16. 16.
    Xie, X., Seung, H.S.: Spike-based learning rules and stabilization of persistent neural activity. In: Advances in Neural Information Processing Systems, pp. 199–208 (2000)Google Scholar
  17. 17.
    Xie, X., Qu, H., Yi, Z., Kurths, J.: Efficient training of supervised spiking neural network via accurate synaptic-efficiency adjustment method. IEEE Trans. Neural Netw. Learn. Syst. 28(6), 1411–1424 (2017)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Department of PhysicsTsinghua UniversityBeijingChina
  2. 2.Huawei TechnologyBeijingChina
  3. 3.Tsinghua National Laboratory for Information Science and Technology (TNList), Department of Computer Science and Technology, Center for Brain-Inspired Computing Research (CBICR)Tsinghua UniversityBeijingChina

Personalised recommendations